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Portable and Predictable Performance on Heterogeneous Embedded Manycores (ARTEMIS 295440) ARTEMIS 2 nd Project Review October 2014 Aerospace Demonstrator.

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Presentation on theme: "Portable and Predictable Performance on Heterogeneous Embedded Manycores (ARTEMIS 295440) ARTEMIS 2 nd Project Review October 2014 Aerospace Demonstrator."— Presentation transcript:

1 Portable and Predictable Performance on Heterogeneous Embedded Manycores (ARTEMIS 295440) ARTEMIS 2 nd Project Review October 2014 Aerospace Demonstrator Ricardo Moreno (TAS-E) 2013-10-22ARTEMIS PaPP Review 20131

2 Contents Rationale Hardware Platform Use case: algorithm CCSDS 122 Parallelization with OpenMP Results and demonstration Contribution to PaPP objectives Next year goals 2014-10-28ARTEMIS PaPP Review 20142

3 Rationale Continuous growth of quality and size of images taken from satellite but, limited downlink bandwidth due to technological limitations Solution: Compression of on-board data prior to transmission to ground At the same time: Deal with strong dependability and safety constraints Multi-client and tedious certification process System deterministic behaviour Goal: increase performance and maintain quality. 2013-10-22ARTEMIS PaPP Review 20133

4 Hardware platform: multicore Leon 2014-10-28ARTEMIS PaPP Review 20144

5 Algorithm CCSDS 122: overview Payload lossy and lossless data compressor for bidimensional images instruments and potentially for multispectral and hyperspectral imagers and sounders Two parts: DWT + BPE 2014-10-28ARTEMIS PaPP Review 20145

6 Algorithm CCSDS 122: OpenMP benefits Shared memory: Pthread and OpenMP preferred against MPI or MCAPI OpenMP preferred: Lower modification of original source Easier to synchronize tasks Hybrid Task/Data parallelism solution: Split two tasks: DWT + BPE Data parallelism exploit within each task Pipelining not implemented due to unbalanced execution time on each task 2014-10-28ARTEMIS PaPP Review 20146

7 Parallelization with OpenMP: First approach 2014-10-28ARTEMIS PaPP Review 20147 FIR ¼ DWT

8 Each 1d DWT iteration is parallelized by using OpenMP paradigm Based on pragmas (for loops) DWT example: 2014-10-28ARTEMIS PaPP Review 20148 Parallelization with OpenMP: First approach

9 Parallelization with OpenMP: Results I First test in x86 platform Intel 3770K Quad-core No BPE parallelization Real images from satellite: 2014-10-28ARTEMIS PaPP Review 20149

10 Parallelization with OpenMP: Results II Amhdal’s law 2014-10-28ARTEMIS PaPP Review 201410

11 Demonstrator: PaPP development system 2014-10-28ARTEMIS PaPP Review 201411

12 Image generation 2014-10-28ARTEMIS PaPP Review 201412

13 TAS-E board : Quad-core Leon3 SoC synthesized in FPGA Real target attached to development PC 2014-10-28ARTEMIS PaPP Review 201413

14 Image download and application execution 2014-10-28ARTEMIS PaPP Review 201414

15 Contribution to PaPP objectives Primary objective 2: Portability of performance across at least two hardware platforms for the application use cases. Portability from x86_64 to Leon platform Primary objective 3: Portability of the software stack across application domains. Portability to aerospace domain Primary objective 4: Software developer productivity is increased OpenMP requires lower programmer skills compared to other parallel programming paradigms 2014-10-28ARTEMIS PaPP Review 201415

16 Next year steps Parallelization of bpe (second part of algorithm) Use of OpenMP tasks (currently for-loops parallelization) Better integration with WP3 tools for performance predictability Evaluation of results 2014-10-28ARTEMIS PaPP Review 201416

17 2014-10-28ARTEMIS PaPP Review 201417

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